Fortnightly - Customer service enhancementshttp://www.fortnightly.com/tags/customer-service-enhancements
enMaximizing Customer Benefitshttp://www.fortnightly.com/fortnightly/2012/01/maximizing-customer-benefits
<div class="field field-name-field-import-deck field-type-text-long field-label-inline clearfix"><div class="field-label">Deck:&nbsp;</div><div class="field-items"><div class="field-item even"><p>Performance measurement and action steps for smart grid investments.</p>
</div></div></div><div class="field field-name-field-import-byline field-type-text-long field-label-inline clearfix"><div class="field-label">Byline:&nbsp;</div><div class="field-items"><div class="field-item even"><p>Paul Alvarez</p>
</div></div></div><div class="field field-name-field-import-bio field-type-text-long field-label-inline clearfix"><div class="field-label">Author Bio:&nbsp;</div><div class="field-items"><div class="field-item even"><p><b>Paul Alvarez</b> is a principal and utility practice leader at MetaVu Inc. He led two independent evaluations of smart grid deployments for Xcel Energy and the Public Utilities Commission of Ohio. is a program manager at the Johnson Controls Institute for Building Efficiency.</p>
</div></div></div><div class="field field-name-field-import-volume field-type-node-reference field-label-inline clearfix"><div class="field-label">Magazine Volume:&nbsp;</div><div class="field-items"><div class="field-item even">Fortnightly Magazine - January 2012</div></div></div><div class="field field-name-field-import-image field-type-image field-label-above"><div class="field-label">Image:&nbsp;</div><div class="field-items"><div class="field-item even"><img src="http://www.fortnightly.com/sites/default/files/article_images/1201/images/1201-FEA2-fig1.jpg" width="676" height="847" alt="" /></div><div class="field-item odd"><img src="http://www.fortnightly.com/sites/default/files/article_images/1201/images/1201-FEA2-fig2.jpg" width="1368" height="811" alt="" /></div><div class="field-item even"><img src="http://www.fortnightly.com/sites/default/files/article_images/1201/images/1201-FEA2-fig3.jpg" width="1362" height="751" alt="" /></div><div class="field-item odd"><img src="http://www.fortnightly.com/sites/default/files/article_images/1201/images/1201-FEA2-fig4.jpg" width="1020" height="487" alt="" /></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>Scores of investor-owned utilities (IOUs) have invested hundreds of millions of dollars to improve distribution capabilities. Now those utilities are beginning to consider how best to utilize the new capabilities. Other IOUs are in testing and strategy development phases. And regulators are considering what role they should play in encouraging IOUs to make prudent grid investments while minimizing risks and maximizing benefits for distribution customers.</p>
<p>As more utilities make smart grid business cases public, and as more independent smart grid performance evaluations are completed,<sup>1</sup> a picture of the principal smart grid customer benefits, costs, risks, and drivers is emerging. Many observers, from the Maryland PSC to the governor of Illinois, have concluded—correctly in the author’s opinion—that the business case for the smart grid is far from being a “no brainer,” and that significant post-deployment efforts are required if benefits are to be maximized. It’s becoming increasingly clear that most investments in smart grid capabilities are different from traditional generation, transmission, and distribution investments in one fundamental respect: commissioning doesn’t automatically translate to customer value.</p>
<p>Traditional utility investments are made, more often than not, to replace aging assets or to meet increases in demand for capacity. Once the case for investment is made, procurement proceeds, assets are placed into service, and customers enjoy the value in terms of improved reliability, reduced emissions, and similar benefits. Many, if not most, smart grid capabilities are different in that utilities must make concerted, post-commission efforts—in organizational changes, operating process redesigns, and customer program development—to maximize value for customers. Variation in time-of-use pricing program designs and adoption rates will impact the level of benefits received by both participating and non-participating customers. The extent and design of interactive volt/VAR control deployment will impact the degree of improvement in distribution efficiency. And the vigor and timing of meter-related staff reductions will impact the amount of O&amp;M savings realized.</p>
<p>To summarize, smart grid benefits are driven in large part by utilities’ design and post-commission implementation choices. In the case of IOUs, these choices are in turn driven largely by regulation. As a result it’s appropriate for customers to ask some tough questions related to the smart grid:</p>
<p>• Is my utility maximizing the value of smart grid investments? And how would I know?</p>
<p>• Who should take the lead in measuring benefits—regulators or IOUs?</p>
<p>• What can regulators do to encourage IOUs to make prudent investments and maximize benefits for customers?</p>
<p>• What can IOUs do to maximize benefits for customers?</p>
<p>Answering these questions will require regulators to establish the conditions necessary to encourage and enable IOUs to maximize customer benefits, and IOUs must make the organizational and operational changes—and develop the customer programs—necessary to maximize those benefits. Failure on the part of either party will result in missed opportunities, needlessly long customer payback periods, and ineffective use of smart grid investment grants funded by U.S. taxpayers.</p>
<h4>Measuring Benefits</h4>
<p>Though safety and environmental benefits have been documented in smart grid implementations, three types of benefits appear to be the most tangible for customers: economic benefits, reliability improvements, and customer service enhancements.</p>
<p>• <i>Economic Benefits:</i> Publicly available information from comprehensive and independent evaluations of smart grid deployment performance, combined with reviews of publicly available smart grid business cases, make it fairly clear that 80 percent to 90 percent of the economic benefits of full smart grid deployments available to customers come from three sources: meter reading and management savings; time-differentiated rate implementation; and distribution efficiency. Though every utility’s experience will vary with situational characteristics and deployment variables, measuring economic benefits in just these three areas is likely to satisfy the 80/20 rule <i>(see Figure 1)</i>.</p>
<p>Measuring meter reading and management savings from AMI deployment is relatively straightforward. The accounts of departments for which reductions in force are anticipated as a result of smart grid deployments can simply be compared pre- and post-deployment to quantify savings. Dollar amounts can be translated into metrics for additional precision, including, for example, meter reading and management costs per meter.</p>
<p>AMI deployments also offer value through time differentiated rates. The most appropriate performance measurement approach should consider the circumstances under which such rates are offered. For example, performance can be measured through customer adoption percentage—likely more appropriate in the case of voluntary or opt-in time differentiated rate offers—though utilities might argue that time differentiated rate participation is only partly under utility control. Another approach is to measure overall impact on demand relative to a baseline—likely more appropriate for default or opt-out rate offers, but useful for measuring the performance of voluntary rate offers as well.</p>
<p>Getting customers to adopt time-differentiated rate offers on a voluntary basis has proven extremely challenging, as most designs increase customer risk and effort. The peak time rebate approach, which features carrots instead of sticks, warrants strong consideration as a result. Some of the research on time-differentiated rate designs indicates that carrot approaches can be just as effective as stick approaches in modifying customer usage behavior.<sup>2 </sup></p>
<p>Integrated volt/VAR control offers significant improvements in aggregate distribution efficiency, reducing the usage of customers located on treated feeders by a couple of percentage points through reduced voltage and optimized power factor. Performance can be evaluated by measuring energy accepted by substations and comparing it to sales volumes billed. Such a measure would also include metering errors, billing errors, and theft, but these revenue capture issues are also subject to improvement through smart grid investments and warrant measurement and performance management efforts.</p>
<p>• <i>Reliability Improvements:</i> Most smart grid deployment plans include improved capabilities in distribution automation and status monitoring designed to improve grid reliability. Independent assessments have confirmed that significant improvements in reliability—moderate double digits as a percentage—are indeed available from these capability improvements. Existing reliability metrics such as SAIDI, SAIFI, and MAIFI<sup>3</sup> are likely sufficient to measure these improvements over time, though observers are cautioned that improvements in SAIDI (resulting from increased sectionalization, for example) can come at the expense of MAIFI performance. “Customer minutes out” is another performance metric that warrants consideration for this reason. Of course normalization for weather will still be an important component of reliability measurements.</p>
<p>Beyond statistics, however, it’s difficult for individual customers to perceive even fairly significant improvements in reliability. The issue is simply one of scale; a 99.95 percent reliability rating translates to only 4.4 hours of customer outage a year. Even a 20 percent improvement on 4.4 hours of outage amounts to less than an hour’s improvement annually. This fact, combined with the infrequent nature of outages, makes reliability improvements extremely difficult for customers to perceive.</p>
<p>• <i>Customer Service Enhancements:</i> Customer service enhancements, generally made possible by AMI and two-way meter communications, can be difficult to measure. Quantifying the percentage of eligible customers that access a new capability is a reasonable metric for some enhancements, such as in the case of detailed energy usage information being made available via secure web page. However performance on other potential customer service enhancements isn’t so easily measured. Consider for example, a proactive outage information service. Such a service would combine smart grid capabilities with today’s communications technologies to text or e-mail information on outages to affected customers. Simple descriptions of new customer service enhancements implemented as part of smart grid deployments might have to suffice as a yes-or-no performance measure in some instances, with emerging best practices serving as useful benchmarks as to what is feasible and valuable. Another service enhancement that a subset of customers would appreciate is prepayment; AMI provides capabilities that facilitate the operation of pay-as-you-go programs.</p>
<h4>Communicating Benefits</h4>
<p>Smart grid benefits can be significant in the aggregate but insufficiently large for individual customers to perceive. Even customer service enhancements, which one might consider to be readily perceptible, are known only to customers that have accessed them or been exposed to them. And even these customers might not relate the enhancements to smart grid investments. Accordingly, documentation and communication of benefits to customers should be a conspicuous component of post-deployment optimization plans and is critical to confirming smart grid merits and value to customers.</p>
<p>One way to think about smart grid benefit communications: If a benefit isn’t communicated, it’s as if the benefit had never been created from a customer’s perspective. Even the U.S. government understands this concept; what driver hasn’t seen a road construction project adorned with “this project funded by the <i>American Reinvestment and Recovery Act</i>” signs?</p>
<p>This isn’t to suggest that communications shouldn’t be conspicuous before smart grid deployment as well. In fact, providing stakeholders with realistic expectations about smart grid value and capabilities before investments are made is perhaps more critical than post-deployment communications. Stakeholder engagement can help utilities prioritize smart grid investments by understanding the value constituencies place on various capabilities and benefits.</p>
<h4>Benefits and Cost Recovery</h4>
<p>Three distinct approaches to smart grid investment cost recovery appear to be emerging: special-purpose riders; special-purpose riders with limits based on anticipated economic benefits; and traditional rate case prudency reviews.</p>
<p>The approach to smart grid cost recovery has significant implications for the roles regulators and IOUs should play in measuring and communicating benefits. Figure 2 depicts the relationship of each approach on the customer-utility risk continuum, and what it means for leadership of benefit measurement and communication efforts.</p>
<p>Some commissions have authorized special-purpose riders to encourage utilities to make smart grid investments. In many cases regulators specify rider characteristics designed to help manage and control smart grid deployment costs. However these riders typically contain few or no quantified provisions designed to maximize benefits for customers. Accordingly, smart grid riders can result in somewhat greater risk to customers than other smart grid cost recovery approaches. In these situations regulators are advised to take a leading role in ensuring that post-deployment benefits are measured, maximized, and communicated to customers.</p>
<p>Other commissions have authorized special purpose riders with built-in customer risk management features. To date, these features have consisted of revenue requirement limitations based on economic benefits that IOUs have suggested would be generated by smart grid investments. Anticipated economic benefits recognized in this manner have included smart grid-related reductions in operations and maintenance spending, improvements in revenue capture, and reduced depreciation expenses associated with beneficial deferral of capital benefits.</p>
<p>An interesting attribute of this approach is that it balances customer and utility risk for post-deployment performance. In so doing, utility shareholders are exposed to increased risk in exchange for increased profit opportunities. To the extent an IOU fails to achieve predetermined levels of benefit, shareholders pay the difference. And to the extent an IOU delivers greater benefits than anticipated, IOU shareholders benefit. In the “rider with limits” case, both regulators and utilities are motivated to measure, maximize, and communicate benefits to customers.</p>
<p>Still other commissions have elected to take no pre-deployment stance on the recovery of smart grid investments, preferring instead to subject IOUs to traditional prudency reviews as part of routine rate case proceedings. This approach can serve to discourage IOU investment in all but the most traditional grid capabilities, as cost recovery of investments in capabilities later determined to have been imprudent could be disallowed. However the approach does minimize risks for customers.</p>
<p>Combination approaches are also available; the Illinois legislature recently approved an act<sup>4</sup> that offers the state’s IOUs the benefits of a rider but retains prudency reviews and adds a performance-based ratemaking component. In the event IOUs fail to hit reliability and revenue enhancement targets, authorized rates of return on smart grid investments can be docked 500 basis points. However, the performance-based measures incorporated in the Illinois legislation fail to include any of the top three economic benefit opportunities—meter reading and management savings; time-differentiated rate implementation; and distribution efficiency.</p>
<p>In smart grid cost recovery frameworks that put utilities at risk, IOUs are encouraged to take a leadership role in smart grid benefit measurement, maximization, and communication, as doing so can result in a significant reduction in cost recovery risk.</p>
<h4>Action Steps for Regulators</h4>
<p>Though their numbers appear to be dropping, there exist some regulators and staffs that are hesitant to provide IOUs with incentives or change rules to encourage activities and investments that arguably could be categorized as IOUs’ social responsibilities. Although this sentiment is understandable, it ignores the reality of the regulatory compact and IOUs’ responsibilities to their shareholders.</p>
<p>Regulators increasingly are embracing the concept of shared responsibility for shaping electric distribution systems and services in a manner that creates the greatest value for utility customers for the least cost. Open and informal interactions with multiple stakeholders are likely to lead to the best outcomes and the most appropriate rulings and rule changes required to release the potential of the smart grid.</p>
<p>The reality is that post-investment regulatory actions will be required to ensure that the benefits of smart grid investments are maximized for customers. Several types of smart grid benefits increase IOUs’ risk or reduce their opportunities to earn authorized rates of return—or both—particularly in states where decoupling hasn’t been introduced. Other types of smart grid benefits will accrue to shareholders until recognized in a general rate case. Further, regulatory rule changes might be required to enable other types of smart grid benefits. Examples of smart grid capabilities and benefits that should prompt regulator action include:</p>
<p>• Distribution efficiency and time-differentiated rates will reduce utility sales volumes.</p>
<p>• Operations and maintenance expense reductions and revenue capture improvements accrue to shareholders until recognized in a general rate case—absent special cost recovery mechanisms.</p>
<p>• Some anticipated economic benefits might not be possible without thoughtful regulatory rule changes.</p>
<p>• New regulatory rules might be required to encourage certain types of customer service enhancements.</p>
<p>Some types of smart grid capabilities reduce sales volumes and therefore a utility’s opportunity to earn its authorized rate of return, absent decoupling or some sort of incentive opportunity. In fact two of the three smart grid capabilities that yield the greatest economic benefits—distribution efficiency and time differentiated rates—will reduce utility sales volumes. Prepayment programs are also likely to reduce sales volumes. Utilities will understandably be reluctant to maximize such benefits. Some would argue that investments in distribution efficiency, time differentiated rate capabilities, and even prepayment programs are the economic equivalent of demand-side management (DSM) programs because, like DSM programs, the utilities make the investment and take the revenue risk while customers benefit. To address utility disincentives to maximizing these customer benefits, regulators could consider decoupling or performance-based ratemaking.</p>
<p>On the other side of the coin, some types of smart grid benefit accrue to shareholders until recognized in a general rate case. Examples of these types of benefits include operations and maintenance spending reductions—<i>i.e.</i>, in meter reading—and improved revenue capture—for example, through improved meter accuracy or reduced theft. Regulators are encouraged to consider revenue requirement reductions, such as the rider limitations described earlier, to ensure customers receive economic benefits in the absence of a timely rate case that would recognize such benefits.</p>
<p>Some smart grid capabilities might not deliver benefits without thoughtful regulatory rule changes. For example many utilities included remote service disconnect capabilities in their AMI designs, along with associated economic benefits in their business cases. Most states’ rules require utilities to contact customers before service is disconnected for reason of non-payment. In most of these states, this requirement has been prescribed to mean in-person contact, versus a phone call, generally to offer a final opportunity to meet a payment plan obligation, or to post a disconnection notice. As a result of these requirements, remote disconnect capabilities don’t result in cost savings in instances of non-payment. If thoughtful compromises can’t be reached, associated cost savings won’t be realized.</p>
<p>Other smart grid capabilities might require new regulatory rules. One of these is proactive outage information, in which enhanced smart grid outage management information can be combined with automated outbound phone messaging, e-mailing, and texting capabilities to keep customers informed about the status of an outage. Although this might sound like a valuable service, customers could come to rely upon the accuracy of such communications and take certain actions based on them. It’s easy to envision how inadvertent inaccuracies in such communications could cost customers money; consider a customer with a freezer full of food who fails to receive a notice about an outage while out of town on vacation or business. Utilities are understandably reluctant to offer new services that might subsequently be transformed into utility obligations and result in potential liabilities. New regulatory rules might help overcome utility resistance to such service improvements.</p>
<p>Another example of a smart grid capability that will require new rules to maximize customer benefits is increased data availability. Regulators will need to establish rules about the privacy and security of energy usage data, as well as rules related to accessing such data by customers and authorized third parties.</p>
<p>To summarize, regulators have many tools at their disposal to encourage utilities to make prudent investments in distribution capabilities while minimizing risks and maximizing benefits to customers.</p>
<h4>Action Steps for IOUs</h4>
<p>Regulators and customers will demand that the benefits of smart grid investments are maximized, and utilities should understand this and act accordingly. Increasing use of emerging measurement standards is contributing to a growing body of knowledge around electric distribution business performance, going beyond reliability and incorporating everything from distribution efficiency and customer service improvements to time differentiated rate participation and impact. Utilities can expect that their feet will be held to the fire.</p>
<p>Utilities will need to make significant organizational and operational changes to truly maximize the value of smart grid investments. From service centers to distribution control centers, from engineering to marketing, and from distribution capacity planning to business systems, roles and responsibilities will need to be modified, operating processes will need to be changed, and programs will need to be developed. A few examples:</p>
<p>• Performance-based ratemaking might dramatically increase the responsibilities of marketing or distribution operations for utility financial performance.</p>
<p>• Smart grid capabilities make possible new frontiers in DSM program portfolios, features, designs, and promotions, and facilitate pre-payment programs.</p>
<p>• Business systems departments will need to develop electrical engineering understanding, while field services personnel will need to learn new information technology skills.</p>
<p>• Resources will need to be reduced in some functions and increased in others.</p>
<p>• New applications and systems integration will be needed to help employees and functions maximize the value of smart grid data.</p>
<p>• Organizational realignments, operating process changes, and incentive modifications will be required to maximize the value of smart grid capabilities.</p>
<p>• Regulatory administration will need to identify and pursue the rule and incentive modifications necessary to enable and encourage maximization of smart grid benefits.</p>
<p>A comprehensive and formal change management plan should be part of every utility’s post-deployment optimization strategy and include organizational, operational, systems, capabilities, and customer program enhancement components <i>(see Figure 4)</i>.</p>
<p>Regulators are currently pre-occupied with a great number of critical issues, namely FERC transmission orders, new and proposed EPA regulations, and associated jurisdictional issues. IOUs face their own challenges, including flat or declining usage, capital constraints, and regulatory uncertainty. However utility customers will be served well if both parties focus some of their resources on maximizing the value of smart grid benefits through regulatory and operational changes. This focus likely will be rewarded with both improved smart grid economics and enhanced services for customers.</p>
<p> </p>
<h4>Endnotes:</h4>
<p>1. The results of independent evaluations of two smart grid deployments led by the author for MetaVu Inc. are available on Colorado and Ohio PUC websites.</p>
<p>2. Ahmad Faruqui and Sergici, Sanem, “Dynamic pricing of electricity in the mid-Atlantic region: econometric results from the Baltimore gas and electric company experiment,” <i>Journal of Regulatory Economics</i>, 2011, vol. 40, issue 1, pp. 82-109.</p>
<p>3. SAIDI = system average interruption duration index; SAIFI = system average interruption frequency index; MAIFI = momentary average interruption frequency index.</p>
<p>4. <a href="http://www.ilga.gov/legislation/publicacts/fulltext.asp?Name=097-0616" target="_blank">Illinois Public Act 097-0616</a></p>
<p>5. EPRI, report #1020342.</p>
</div></div></div><div class="field-collection-container clearfix"><div class="field field-name-field-sidebar field-type-field-collection field-label-above"><div class="field-label">Sidebar:&nbsp;</div><div class="field-items"><div class="field-item even"><div class="field-collection-view clearfix view-mode-full field-collection-view-final"><div class="entity entity-field-collection-item field-collection-item-field-sidebar clearfix">
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<div class="field field-name-field-sidebar-title field-type-text field-label-above"><div class="field-label">Sidebar Title:&nbsp;</div><div class="field-items"><div class="field-item even">Smart Grid Performance Measurement</div></div></div><div class="field field-name-field-sidebar-body field-type-text-long field-label-above"><div class="field-label">Sidebar Body:&nbsp;</div><div class="field-items"><div class="field-item even"><!--smart_paging_autop_filter--><!--smart_paging_filter--><p>Several sets of guidelines are emerging as the standards in smart grid performance measurement. “A Methodological Approach for Measuring the Costs and Benefits of Smart Grid Demonstration Projects,” available from The Electric Power Research Institute,5 provides a valuable guide to cost and benefit quantification. The “Smart Grid Maturity Model,” developed by the U.S. Department of Energy and Carnegie Mellon University, is ideal for assessing the ability of a utility organization to maximize the value of smart grid investments; the model examines leading indicators, such as the existence and sophistication of smart grid-related operations planning, training, performance measurement, incentives, and similar processes. And the Environmental Defense Fund has weighed in with “Evaluation Framework for Smart Grid Deployment Plans,” which describes a relevant set of outcome reporting metrics—lagging indicators—that could serve to benchmark any electric distribution company’s performance improvement efforts, regardless of smart grid status.</p><p>State regulators have been busy considering smart grid benefits as well. Several orders and investigative dockets provide helpful background for regulators (and IOUs) considering smart grid benefit maximization:</p><p>Illinois Statewide Smart Grid Collaborative Report, Sept. 30, 2010, including an excellent summary of smart grid cost recovery issues.</p><p>Colorado PUC order C11-0406, concluding an investigatory docket that addressed smart grid and advanced metering technologies and associated benefit maximization.</p><p>California PUC order 08-12-009, addressing access to, and the privacy and security of, customer energy usage data.</p><p>Oklahoma Corporation Commission order 576595, approving Oklahoma Gas and Electric’s smart grid rider with adjustments for anticipated benefits, and mandating customer communications.</p><p>Illinois Power Agency Act 097-0616, which reduces the authorized rate of return on smart grid investments in cases in which certain anticipated benefits aren’t achieved.–<em><strong><span><span class="bolditalic">PA</span></span></strong></em></p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p><span>Demand Response Drivers </span></p><p><span>Identifying correlations between adoption rates and market factors. </span></p><p><span>While demand response has gained traction among stakeholders in the electricity industry—<i>e.g.</i>, utilities, regulators, customers and service providers—the adoption of demand response has varied significantly across geographies. Some regions have embraced the concept and show remarkable results; one electric cooperative in Minnesota reports that nearly half of its 700,000 residential customers are enrolled in a program that allows utilities to remotely cycle off their air conditioners during peak events.<sup>5</sup> In contrast, other areas have developed little or no demand response. </span></p><p><span>Why has demand response has taken strong hold in some places and been absent in others? Analyzing a set of potential drivers and the levels of DR achieved sheds some light on the historical evolution of demand response in recent years, and serves efforts to characterize the factors that will influence demand response in the future. </span></p><p><span>The analysis presented here utilizes publicly available data to assess relationships between demand response participation and potential DR drivers<i> (see Figure 1)</i>. This work isn’t a rigorous and comprehensive statistical analysis. Rather, the objective is to identify possible drivers in a qualitative way and test their impact by examining basic patterns in the data. Further, many of the parameters are related and can’t be interpreted as independent drivers of DR. For example, a state might have low electricity prices because it has a high reserve margin and few reliability concerns, possibly because of a large share of base-load generation capacity. Although these characteristics are assessed separately in the analysis, it’s clear they are dependent upon each other and must be considered together to present a complete picture of the drivers of DR in that state or region. In addition, factors that don’t reveal a correlation in recent data might be emerging as drivers, and a similar analysis performed two or three years from now might give different results. </span></p><h4><span>Cost of Electricity </span></h4><p><span>One of the natural places to look for a driver for demand response is the price of power, especially during peak times. By calling on customers to curtail load, utilities and grid operators can avoid the need to purchase expensive peak power on the spot markets. In theory, regions with high costs of electricity would benefit the most from demand response and should therefore pursue more programs and resources. Similarly, customers in high-price systems should have a stronger incentive to enroll as they can receive higher monetary benefits through shedding load. A further benefit of DR to customers is the potential for wholesale price mitigation, which is the overall lowering of the marginal electricity price during DR events due to a downward shift in the demand curve. In markets where supply is tight, this impact on market prices can be significant.<sup>6 </sup></span></p><p><span>A simple proxy for the total (or “all-in”) cost of power is the average retail electricity price,<sup>7</sup> available at the state level through the U.S. Energy Information Administration. Average price is different from peak price and therefore isn’t the ideal variable, but is analyzed due to data limitations. The plot of DR enrollment against retail price of electricity is presented in Figure 2. For the 49 data points in this set (50 states and Washington, D.C., excluding outliers<sup>8</sup>) there’s a positive correlation between average electricity price and DR enrollment.<sup>9</sup></span></p><p><span>Despite this apparent correlation, however, there are important subtleties behind the numbers suggesting that the retail price of electricity isn’t the only factor in driving DR. Some states, such as Hawaii—which is excluded as an outlier—have high electricity prices but no significant demand response resources. This could be due to customer attributes—<i>e.g.</i>, low saturation of central air conditioning—or the evolution of policy supporting demand-side measures in these states. </span></p><p><span>Market structure also plays a role. In restructured states where electric generation is separated from retailer providers—yellow points in Figure 2—the correlation between average price and level of demand response is strong. However, states with regulated wholesale markets—blue points—show no correlation. </span></p><p><span>A wide distribution and relatively low R-squared value suggest that, although average retail price appears to be significant, there are certainly other factors impacting the level of demand response enrolled in a particular state. </span></p><h4><span>Electricity Market Structure </span></h4><p><span>The economics and the logistics of demand response vary significantly across the U.S. and in other developed countries. While the basic concept is consistent everywhere, there are numerous variations in the details, such as communication formats between grid operators and customers, notification times, event-triggered incentives or time-dependent prices, and measurement and verification requirements. Market structure is important to demand response; in many cases an organized wholesale power market facilitates demand response, and the rules and conditions of the particular market define the opportunity for customers, enhancing or hindering participation. The power market can be categorized in three primary ways, which aren’t mutually exclusive. Each is correlated with DR penetration. </span></p><p><span>1) <i>Wholesale market restructuring:</i> With 18 states at some stage of restructuring—generation assets owned by companies other than utilities—there’s an opportunity for demand reductions to participate in the supply side of the market. This factor has become more important through the efforts of the FERC, which has sought for equality between supply- and demand-side resources in the interstate power markets it regulates.<sup>10 </sup></span></p><p><span>2) <i>Retail competition:</i> Breaking from the traditional vertically integrated utility model, 19 states have created competitive retail markets for electricity in which each customer can choose from multiple suppliers. Through competition, retailers can create innovative tariffs and rate structures, suggesting the possibility for attractive benefits for customers able to shed load. </span></p><p><span>3) <i>Presence of ISO/RTO:</i> Approximately two-thirds of electricity customers in the U.S. are located within the regions of independent system operators (ISO) or regional transmission organizations (RTO), which are responsible for ensuring the lights stay on.<sup>11</sup> Several of these entities—PJM, ISO-NE, NYISO, and ERCOT—have emerged as important players in DR by facilitating the market transactions that allow demand response resources to benefit from their participation. The ability for DR to participate in the capacity market is particularly important, as avoided capacity cost is typically the primary financial benefit that DR programs provide. </span></p><p><span>For each of these factors, market structure is correlated with higher levels of demand response. As shown in Figure 3, the DR resources enrolled in a state are highest when restructuring, retail competition, an ISO or RTO, or a combination of these is present. </span></p><p><span>As expected, there’s significant overlap between the states comprising these groups. These correlations aren’t mutually independent, but can be viewed as a general trend toward an open, competitive and structured wholesale power market. The relative success of demand response in regions with these characteristics suggests that similar markets could be good candidates for demand response.<sup>12 </sup></span></p><p><span>However, the relative immaturity of demand-side resources’ participation in wholesale electricity markets leaves questions. For example, how much of the DR resources reported by the market players are truly unique, as opposed to double-counting potential load reductions that are enrolled in other programs? Also, some market rules allow distributed generation—<i>e.g.</i>, diesel-fired backup generators—to enroll as demand response, while others don’t. Both of these factors suggest that the levels of DR reported might be overstating the actual load reduction available to the grid. In addition, the market mechanisms that determine the conditions for demand response participation are in flux; how will the rapid growth of DR in wholesale markets affect the broader picture—<i>e.g.</i>, prices, auction results, etc.? </span></p><p><span>Market structure appears to be important for cultivating demand response, but it isn’t the only factor. In fact, DR has been shown to thrive in regions that haven’t been fully restructured. In California, for example, regulatory initiatives and the efforts of utilities and their contractors have led to significant impacts—approximately 5 percent of peak demand—despite the absence of a centralized capacity market.<sup>13 </sup></span></p><p> </p><h4><span>Strength of Policies and Regulation </span></h4><p><span>While the structure of the electricity market and its operating conditions provide a foundation for demand response, there are also policies that affect the uptake of DR in a particular state. In the U.S., it’s common for electric distribution utilities—especially investor-owned utilities but also cooperatives—to be regulated by a commission of elected or appointed officials. In addition, many state legislatures have mandated improvements in energy efficiency and other demand-side initiatives, with an increase of this trend in recent years. Even in so-called “deregulated” jurisdictions, FERC is active in defining the rules by which the markets are run. Policy is therefore a crucial component of the DR landscape in any area. </span></p><p><span>Two data sources are employed to examine the impact of policy on the penetration of DR: 1) legislative or regulatory actions that directly support demand response, as reported in a legislative primer document produced by the Demand Response Coordinating Council, including state-by-state assessments of DR policy in the U.S.;<sup>14</sup> and 2) the general legislative climate supporting energy efficiency in the state, as analyzed by the ACEEE state scorecard.<sup>15</sup></span></p><p><span>Both strong demand response policy and a high energy efficiency score correlate with higher levels of demand response. Figure 4 shows the average DR levels for states with and without devoted DR policy, indicating that states with supportive demand-side policy have more than double the penetration of DR than those without, with a statistical confidence of 95 percent.<sup>16</sup></span></p><p><span>A similar effect is evident when comparing the energy efficiency scorecard ratings of states—a general indication of a state that takes a proactive approach to demand-side initiatives. As displayed in Figure 5, states with a higher score for energy efficiency also see higher levels of demand response, even though the ACEEE rating doesn’t explicitly account for demand response. </span></p><p><span>Interestingly, the impacts of DR policy and energy efficiency policy are similar when it comes to driving demand response participation. This can partially be explained by the cognitive link between these two from both the utility and policy perspectives. In many organizational structures, both demand response and energy efficiency are included as “demand-side management,” and indeed there’s significant overlap between the states with DR policy and those scoring high on the energy efficiency rating. </span></p><p><span>Again, neither of these correlations is strong enough for policy to be considered the only factor shaping the penetration of demand response. But directionally, it’s clear that policy plays a role. </span></p><h4><span>Generation Mix—Hydroelectric Power </span></h4><p><span>An interesting exception to the link between progressive policy and demand response is the Pacific Northwest, where regulators, utilities, and other interested stakeholders have been pursuing efficiency for decades and yet have little DR participation today.<sup>17</sup> One explanation for this discrepancy is the high quantity of hydroelectric generation in the region, which provides significant peaking capacity but is energy constrained. In addition to the renewable nature of the resource, hydro power has built-in energy storage capacity, as operators can adjust the flow of water through turbines to accommodate changing demand. </span></p><p><span>By relying on its inherent storage capabilities, hydroelectric operators alleviate peak demand issues and thereby reduce the need for demand response. This seems to be a factor in the evolution of demand response in the Northwest, evidenced by the tail of high-hydro, low-DR points in Figure 6. An important note here is the tipping point at which hydroelectric resources can no longer provide adequate flexibility for the system. Operators in hydro-heavy regions are seeing these events with increasing frequency in the face of growing load, annual fluctuations in water levels, and increasing policy constraints on hydro plant operations. As a result, decision-makers in these regions are turning to demand response to mitigate risk and improve system economics. </span></p><h4><span>Reserve Margin and Reliability </span></h4><p><span>One of the most frequently mentioned benefits of demand response is its ability to alleviate short-term reliability concerns on the electric grid. With aging infrastructure and rapidly increasing demand for power in many parts of the U.S., balancing supply and demand has presented a challenge. </span></p><p><span>Because of its ability to be quickly deployed without major infrastructure investments, demand response has been proposed as one solution to maintain sufficient reserve margins.<sup>18</sup> To test the adoption of this theory in practice, annual reported reserve margins from 22 unique NERC regions and sub-regions are compared to NERC data on demand response for the same regions, between 2003 and 2010 <i>(See Figure 7)</i>. </span></p><p><span>As expected, regions with higher reserve margins have lower levels of demand response. The correlation—logarithmic for best fit—is much weaker than those in Figures 2 through 6; there are many instances of low DR coupled with slim reserve margins and high DR levels with healthy reserve margins. However, the directional relationship in these results suggests that, at least to a small degree, the designers and operators of power markets are viewing DR as a viable option for managing the grid. This uptake is remarkable considering the strong emphasis on reducing risk in the power industry; DR has very limited performance data when compared to conventional generation assets, and those concerned with grid reliability raise questions about the duration of demand response resources beyond the short (one-to-three year) contracts typical among today’s participants. </span></p><p><span>As noted above, demand response in the last decade has been characterized primarily by its use as a capacity resource, with curtailments of several consecutive hours—<i>e.g.</i>, hot summer afternoons. While DR appears to have been utilized as a solution for conventional peak demand issues, the rise of renewable portfolio standards and other policies are quickly changing market dynamics and system needs. With high fractions of electricity provided by variable energy resources such as wind and solar, it could be necessary to compensate for unexpected variations of up to 20 or 30 percent of the total system load on a short timescale—when clouds cover solar panels or wind drops suddenly.<sup>19</sup> In the future, the reliability value of demand response has potential for alleviating these problems, “any day, any time.”<sup>20 </sup></span></p><p> </p><h4><span>Missing Links </span></h4><p><span>While the factors explored above appear to have had an impact on the evolution of demand response adoption, several other potential drivers don’t reveal a correlation with the level of DR. This doesn’t necessarily rule out these factors as possible explanatory variables for DR adoption rates. Rather, additional analysis is needed to better understand their relationship to DR market penetration in the past, and could reveal correlations in the future. In some cases, these drivers might represent untapped opportunities for demand response. </span></p><p><span>• <i>Weather:</i> DR is often suggested as a mitigating solution for peak electrical demand caused by large cooling loads on hot days. However, average summer temperature extremes show no correlation with levels of DR participation. One explanation for this is a discrepancy in the data. DR participation is expressed in terms of enrollment, which would depend on system planning that typically assumes average weather conditions. It’s likely that DR events are called more often during times of extreme temperatures, and therefore a correlation might emerge if performance data, rather than enrollment, were available. </span></p><p><span>• <i>Frequent outages:</i> To test the possibility that demand response has been implemented to mitigate blackouts and brownouts, the analysis compared DR levels with the frequency and impact of reliability events. The data source for reliability is a paper from Lawrence Berkeley National Laboratory that tracked the reliability of the U.S. electric power system.<sup>21</sup> No correlation is evident. However, the fact that calls to DR resources are included in the emergency protocols for many electric systems proves that demand response is utilized as a reliability “backstop.”<sup>22 </sup></span></p><p><span>• <i>Load and population growth:</i> In places where growth is significant, demand response has been recommended as an alternative to building new capacity. However, examination reveals no evidence that areas with stronger growth—both electric load and population—have achieved higher levels of DR. The state-level forecasts in the National DR Potential Study provides the data source.<sup>23 </sup></span></p><p><span>• <i>DR incentive levels:</i> Perhaps surprisingly, the level of utility incentives provides for demand response doesn’t appear to be a factor in driving participation. To test incentive level, the analysis compared the average incentive for peak reductions ($/MW) reported in the Energy Information Agency’s Form 861.<sup>24 </sup></span></p><p><span>• <i>Customer attributes:</i> Demand response has been particularly successful among certain sectors and customers with particular attributes. For example, large industrial customers provide bulk reductions under interruptible tariffs, and residential air conditioners are important for direct load control programs. However, the data analyzed for this report haven’t revealed a significant role for customer attributes in driving overall DR levels. Using data from the FERC potential assessment, the analysis examines sectoral mix (residential, commercial, and industrial) and saturation of residential central air conditioners. Neither shows a correlation with DR participation. </span></p><h4><span>Future Drivers of DR Adoption </span></h4><p><span>Several potential future uses of DR could act as drivers of program adoption in the longer term. Expanding markets, increased adoption of technology, energy and climate policy, and even economic recovery are capable of affecting the future of demand response in the coming decade. </span></p><p><span>While demand response has evolved from interruptible power arrangements between utilities and large industrial customers and direct load control programs that cycle off residential air conditioning, it has been slow to penetrate the bulk of the commercial sector, where customers insist on maintaining control over their operations and require attractive terms to participate. As the marketplace evolves through innovative business models and enabling technology, more customers will be interested in an expanding array of DR opportunities, expanding the resource around the world. <sup>25 </sup></span></p><p><span>As economies evolve toward reduced greenhouse gas emissions and low-carbon growth, there’s a need for technology and market solutions that enable this change. Demand response is part of a more flexible electricity system, allowing both supply and demand to interact frequently and at scale. In a carbon-constrained world, this flexibility can shift generation away from greenhouse gas emitting sources and therefore reduce carbon emissions in a meaningful way.<sup>26 </sup></span></p><p><span>Also, in many U.S. states and European nations, significant amounts of renewable, variable energy resources are expected to come online in the next five to 10 years. Grid operators are tasked with identifying cost-effective ways to integrate these variable resources into the market without sacrificing system reliability. DR is being considered as one potential solution. In particular, automated—<i>i.e.</i>, technology-based—DR, such as direct load control, has the potential to provide fast response that could participate in ancillary services markets, such as spin or even regulation.<sup>27 </sup></span></p><p><span>Wholesale energy markets also play an increasing role. In addition to the capacity markets that have been central to the development of DR in parts of the United States, wholesale market operators administer energy markets in which participants—traditionally power generators or day-traders—buy and sell power on an hourly or more frequent basis. Some of these markets have opened up for DR resources to participate, and some proposed policies would encourage the inclusion of demand response in energy markets across the country.<sup>28</sup></span></p><p><span>Likewise, retail competition and metering technologies are fostering the expansion of DR options. As advanced metering infrastructure continues to be deployed to customers across the U.S. and internationally, many retailers will be taking full advantage of the new capability that this infrastructure offers by providing customers with innovative rate designs and technologies that are designed to produce more responsive demand. </span></p><p><span>Another potentially valuable future use of DR is to encourage efficient charging patterns in regions with high levels of plug-in electric vehicle (PEV) adoption. Left uncontrolled, PEV charging could lead to significant increases in the system peak, as owners return from work in the early evening and plug in their vehicles. A well designed time-of-use rate could encourage charging during lower-priced off-peak hours. Additionally, direct control of the charging devices could be used to address location-specific reliability issues caused by unexpected levels of PEV charging. </span></p><h4><span>Conclusion and Next Steps </span></h4><p><span>Energy price levels, market structure, demand-side policy, generation mix, and reserve margin all appear to have an impact on the market penetration of DR programs. However, none of these relationships represents a strong correlation, suggesting that the reality of DR evolution relies on a combination of these and other drivers. Such factors as demand growth rate and outage frequency also could play a role, but don’t reveal correlations in the data examined here; they require further exploration. Additionally, future drivers like renewables integration and PEV charging don’t appear in the historical data but could potentially influence the path that DR takes in the longer term. </span></p><p><span>Important next steps will involve further research. Specifically, the quantitative approach for determining the extent to which each of these drivers influences adoption could be improved through a more rigorous econometric analysis. This approach would involve developing a single regression model, with the various drivers of demand response as explanatory variables of DR market penetration. Such an approach would allow accounting for interactions between the explanatory variables and developing an estimate of the relative predictive power of each variable. </span></p><p><span>One challenge in this approach is the availability and resolution of the necessary data. While data on some DR drivers are available at the state level, others are only available at the regional, ISO, or utility level. Mapping the data to a consistent level of geographic granularity—and filling in the data gaps—is feasible and likely to produce new insights and findings, but would require further research and analysis. This approach would require a sophisticated analytical framework to account for the relative importance of variables that aren’t independent—<i>e.g.</i>, policy affects market structure, and reliability affects price. Introducing further granularity in the analysis could also lead to new insights—for example, distinguishing between price-triggered DR and reliability-triggered DR and separately quantifying the impact of the drivers of each. </span></p><p><span>Ultimately, this analysis is a starting point; an understanding of the historical adoption of demand response will help identify settings where advancing demand response will be valuable. A key pivot in the DR landscape is the role of policy. In restructured, competitive markets, the rulemaking process of regulatory entities can facilitate demand response by allowing it to participate alongside electric supply. In vertically integrated regions, regulatory actions can align incentives for utilities and legislation can drive demand response programs. </span></p><p><span>With an important stake in the development of DR in the future, policy-makers at state, regional, and national levels can benefit by analyzing the experience of the past. In addition, this analysis can support utility planners, service providers and other stakeholders seeking to expand the influence of this resource as we move toward the smart grid of the future. </span></p><p> </p><h4><span>Endnotes: </span></h4><p><span>1. FERC “2010 Assessment of Demand Response and Advanced Metering,” 2011. </span></p><p> </p><p><span>2. FERC “2008 Assessment of Demand Response and Advanced Metering,” 2008. </span></p><p><span>3. The “Full Participation” (<a href="http://www.ferc.gov/legal/staff-reports/06-09-demand-response.pdf">http://www.ferc.gov/legal/staff-reports/06-09-demand-response.pdf</a>) scenario from FERC “A National Assessment of Demand Response Potential,” 2009. </span></p><p><span>4. Ahmad Faruqui, Ryan Hledik, Sam Newell, and Hannes Pfeifenberger, “The Power of Five Percent,” <i>The Electricity Journal</i>, October 2007. </span></p><p><span>5. See <a href="http://www.conservationminnesota.org/news/?id=2618">http://www.conservationminnesota.org/news/?id=2618</a> </span></p><p><span>6. The Brattle Group, “Quantifying Demand Response Benefits in PJM,” prepared for PJM Interconnection, LLC, and the Mid-Atlantic Distributed Resources Initiative (MADRI), January 29, 2007. </span></p><p><span>7. Average price of power is distinct from the cost to supply peak electricity, and often depends on <i>ex ante</i> regulatory processes. Unfortunately, a comparable dataset on peak power costs isn’t available for this analysis. </span></p><p><span>8. In this and other analyses presented in this report, outliers are defined as points three or more standard deviations away from the mean. </span></p><p><span>9. A value of 1.0 would indicate that the electricity price and demand response adoption are perfectly related in a linear fashion. A value of 0 would indicate that there’s no linear relationship between the two variables, and a negative value (between 0 and -1.0) indicates an inverse relationship between the two variables. </span></p><p><span>10. Federal Energy Regulatory Commission Order 719, “Wholesale Competition in Regions with Organized Electricity Markets,” 18 CFR Part 35, October 2008. </span></p><p><span>11. See <a href="http://www.isorto.org">http://www.isorto.org</a> </span></p><p><span>12. This will, however, still depend on the specific system conditions in the region; if a region with a restructured power market has, for example, very high reserve margins, low capacity market prices, or both, DR might have lower market penetration. </span></p><p><span>13. In California, bilateral contracts for capacity are established in order to meet a state-mandated resource adequacy requirement. </span></p><p><span>14. U.S. Demand Response Coordinating Council, “Demand Response and Smart Metering Policy Actions since the Energy Policy Act of 2005 (<a href="http://www.ncouncil.org/Documents/NCEP_Demand_Response_12081.pdf">http://www.ncouncil.org/Documents/NCEP_Demand_Response_12081.pdf</a>),” prepared for the National Council on Electricity Policy, Fall 2008. </span></p><p><span>15. Maggie Eldridge, Max Neubauer, Dan York, Shruti Vaidyanathan, Anna Chittum, and Steve Nadel, “The 2008 State Energy Efficiency Scorecard (<a href="http://www.aceee.org/research-report/e086">http://www.aceee.org/research-report/e086</a>),” American Council for an Energy Efficient Economy (ACEEE), 2008. </span></p><p><span>16. Testing the statistical confidence of this effect involves hypothesis testing based on t-score statistics, a method suited to small sample sizes. This method assumes a normal distribution for the two sets of data: with and without DR policy. </span></p><p><span>17. DR has fluctuated over the years in the region served by the Bonneville Power Administration. In Oregon, for example, there are 5 MW of non-generation demand response resources today. However, during the Western Electricity Crisis of 2000-’01, estimated DR reached as high as 335 MW. See Lisa Schwartz, “Demand Response Programs for Oregon Utilities,” Oregon Public Utilities Commission, 2003. </span></p><p><span>18. Reserve margin is a measure of electric system health, representing the degree to which the capacity of the system exceeds the expected peak demand. For this report, a simple reserve margin is calculated using data from NERC; for each region, reserve margin is the total capacity divided by the internal demand, minus one. </span></p><p><span>19. In 2008, a drop in wind power combined with other factors to create a reliability condition in Texas. Within 10 minutes of the shortfall, more than 1,100 MW of demand response resources were activated, alleviating a potential blackout and allowing the system to recover. See <a href="http://www.reuters.com/article/2008/02/28/">http://www.reuters.com/article/2008/02/28/</a> us-utilities-ercot-wind-idUSN2749522920080228 </span></p><p><span>20. The phrase “any day, any time” originates from the Demand Response Research Center at Lawrence Berkeley National Laboratory, where researchers are increasingly interested in “Fast DR” as an ancillary service. </span></p><p><span>21. Joseph Eto and Kristina Hamachi LaCommare, “Tracking the Reliability of the U.S. Electric Power System (<a href="http://eetd.lbl.gov/EA/EMP/reports/lbnl1092e-puc-reliability-data.pdf">http://eetd.lbl.gov/EA/EMP/reports/lbnl1092e-puc-reliability-data.pdf</a>),” Lawrence Berkeley National Laboratory, October 2008. </span></p><p><span>22. Personal correspondence with John Moura at the North American Electric Reliability Corporation, January 2011. </span></p><p><span>23. “A National Assessment of Demand Response Potential (<a href="http://www.ferc.gov/legal/staff-reports/06-09-demand-response.pdf">http://www.ferc.gov/legal/staff-reports/06-09-demand-response.pdf</a>),” FERC, 2009. </span></p><p><span>24. U.S. Dept of Energy, Energy Information Agency Form 861, File 3 (<a href="http://www.eia.doe.gov/cneaf/electricity/page/eia861.html">http://www.eia.doe.gov/cneaf/electricity/page/eia861.html</a>), 2008. </span></p><p><span>25. Kelly Smith and Michelle Quibell, “Technology in Commercial Buildings: A Key to Scaling up Demand Response (<a href="http://www.institutebe.com/InstituteBE/media/Library/Resources/Smart%20percent20Grid_Smart%20percent20Building/Issue-Brief%E2%80%94-Scaling-Up-Demand-Response.pdf">http://www.institutebe.com/InstituteBE/media/Library/Resources/Smart%20p...</a>),” Institute for Building Efficiency, July 2010. </span></p><p><span>26. Meg Gottstein and Lisa Schwartz, “The Role of Forward Capacity Markets in Increasing Demand-Side and Other Low-Carbon Resources (<a href="http://raponline.org/docs/RAP_Gottstein_Schwartz_RoleofFCM_ExperienceandProspects2_2010_05_04.pdf">http://raponline.org/docs/RAP_Gottstein_Schwartz_RoleofFCM_Experienceand...</a>),” Regulatory Assistance Project, May 2010. </span></p><p><span>27. Theodore Gates Hesser, “The Future of Demand Response,” prepared for the Natural Resources Defense Council, September 2010. </span></p><p><span>28. FERC Notice of Proposed Rulemaking, 18 CFR Part 35 (<a href="http://www.ferc.gov/EventCalendar/Files/20100802113647-RM10-17-000.pdf">http://www.ferc.gov/EventCalendar/Files/20100802113647-RM10-17-000.pdf</a>), August 2010. </span></p><p> </p><p> </p></div></div></div> </div>
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<a href="/tags/ahmad-faruqui">Ahmad Faruqui</a><span class="pur_comma">, </span><a href="/tags/aif">AIF</a><span class="pur_comma">, </span><a href="/tags/american-reinvestment-and-recovery-act">American Reinvestment and Recovery Act</a><span class="pur_comma">, </span><a href="/tags/ami">AMI</a><span class="pur_comma">, </span><a href="/tags/ami-deployments">AMI deployments</a><span class="pur_comma">, </span><a href="/tags/benefits">Benefits</a><span class="pur_comma">, </span><a href="/tags/cost">Cost</a><span class="pur_comma">, </span><a href="/tags/customer-service">Customer service</a><span class="pur_comma">, </span><a href="/tags/customer-service-enhancements">Customer service enhancements</a><span class="pur_comma">, </span><a href="/tags/demand-response">Demand response</a><span class="pur_comma">, </span><a href="/tags/distribution">Distribution</a><span class="pur_comma">, </span><a href="/tags/distribution-efficiency">Distribution efficiency</a><span class="pur_comma">, </span><a href="/tags/dr">DR</a><span class="pur_comma">, </span><a href="/tags/dsm">DSM</a><span class="pur_comma">, </span><a href="/tags/dynamic-pricing-0">Dynamic pricing</a><span class="pur_comma">, </span><a href="/tags/economic-benefits">Economic Benefits</a><span class="pur_comma">, </span><a href="/tags/economics">Economics</a><span class="pur_comma">, </span><a href="/tags/epa">EPA</a><span class="pur_comma">, </span><a href="/tags/epri">EPRI</a><span class="pur_comma">, </span><a href="/tags/ferc">FERC</a><span class="pur_comma">, </span><a href="/tags/grid-reliability">grid reliability</a><span class="pur_comma">, </span><a href="/tags/ious">IOUs</a><span class="pur_comma">, </span><a href="/tags/maifi">MAIFI</a><span class="pur_comma">, </span><a href="/tags/maryland-psc">Maryland PSC</a><span class="pur_comma">, </span><a href="/tags/metavu-inc">MetaVu Inc.</a><span class="pur_comma">, </span><a href="/tags/performance-based-rate">Performance-based rate</a><span class="pur_comma">, </span><a href="/tags/performance-based-ratemaking">Performance-based ratemaking</a><span class="pur_comma">, </span><a href="/tags/recovery">Recovery</a><span class="pur_comma">, </span><a href="/tags/reliability">Reliability</a><span class="pur_comma">, </span><a href="/tags/said">SAID</a><span class="pur_comma">, </span><a href="/tags/saidi">SAIDI</a><span class="pur_comma">, </span><a href="/tags/saifi">SAIFI</a><span class="pur_comma">, </span><a href="/tags/smart-grid">Smart grid</a><span class="pur_comma">, </span><a href="/tags/stakeholder-engagement">Stakeholder engagement</a> </div>
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